Bronchoscopy is a medical procedure commonly used in lung-cancer assessment[1]. Lung-cancer assessment involve two main stages[2, 3, 4]: 1) three-dimensional (3D) multi-detector computed-tomography (MDCT) image assessment; and 2) live bronchoscopy. During MDCT assessment, the physician uses the two-dimensional (2D) transverse slices obtained from the patient's MDCT scan to identify specific diagnostic regions of interest (ROIs), such as lymph nodes and suspicious nodules[5, 6, 7]. In this step, the physician also identifies the closest route to each ROI and mentally plans a 3D route using the 2D slices. During bronchoscopy, the physician maneuvers a flexible bronchoscope through the lung airways towards each ROI along the pre-planned mentally defined route. This is done by identifying the bifurcations along the route on the live endoluminal video feed obtained from the bronchoscope. This manually-based route planning procedure proves to be challenging, resulting in errors in bronchoscopy as early as the second airway generation[8, 9].
Image-guided bronchoscopy guidance systems enable more accurate bronchoscopy [4, 10, 11, 12, 13]. These systems are motivated by virtual bronchoscopy (VB), wherein the 3D MDCT image of a person's chest serves as a “virtual environment”[14, 15, 16]. A software-defined virtual camera navigates through the lungs in the virtual environment and presents endoluminal renderings of the 3D data, also known as VB images. To facilitate guidance during bronchoscopy, all bronchoscopy guidance systems rely on some method for registration of the real bronchoscope in 3D surgical space to the 3D MDCT virtual space. Based on the type of sensor used for registration, bronchoscopy guidance systems can be either electromagnetic (EM) or image-based[3, 4, 11, 12, 13, 15, 16, 17, 18].
An EM-based guidance system consists of the following: 1) an EM field generator; 2) a steerable EM probe; and 3) guidance software[12, 13, 17]. The EM field generator generates an EM field around the patient's chest. The steerable EM probe is inserted through the working channel of the bronchoscope and tracked in the external EM field. Prior to the start of bronchoscopy, the steerable probe is used to calibrate and synchronize the coordinate system of the external EM field and the MDCT coordinate system. Thus, as the EM probe is tracked during bronchoscopy, its position in the MDCT coordinate system becomes nominally known. Such a system allows for immediate establishment of the global position of the bronchoscope tip within the 3D MDCT coordinate system. However, metallic objects in the vicinity induce ferromagnetic device interference, leading to distortions in the external EM field[19]. Moreover, the patient's breathing causes chest movement that leads to registration errors[20]. These errors are magnified in the peripheral airways, as the airway branches become smaller and move with the patient's breathing. Furthermore, once the bronchoscope is guided to the ROI, the steerable probe has to be retracted from the working channel of the bronchoscope so that the biopsy tools can be inserted to collect ROI tissue samples. Thus, EM-based bronchoscopy guidance systems implicitly provide global registration, but suffer in local registration. There has been ongoing research to combine the EM and image-based guidance methods in an attempt to mitigate these problems [21, 22].
Image-based bronchoscopy guidance systems rely on volume-rendered[16, 23] or surface-rendered[3, 4, 24, 25] endoluminal images of the airway tree from the 3D MDCT scans in order to establish the location of the bronchoscope. This is generally done by comparing the VB images with the real bronchoscopic (RB) video frames. Weighted normalized sum of square difference errors (WNSSD)[24] and normalized mutual information (NMI)[3, 4, 26] are metrics that are used for comparing the images obtained from the two sources. Registration is carried out using Powell's optimization, simplex or gradient methods. The image-based bronchoscopy guidance methods rely on local registrations at bifurcations and so are less susceptible to patient breathing motion. However, as these methods rely on the bronchoscope video, they are affected by artifacts in bronchoscope video caused by patient coughing or mucous obstruction. Also, most of the available systems rely on manual registration for initialization of the bronchoscope position. During a live bronchoscopic procedure, the absence of a global registration algorithm leads to increased procedure time and some uncertainty in the bronchoscope position. This in turn leads to guidance errors. Thus, image-based bronchoscopy guidance methods implicitly provide excellent local registration, but no global registration.
Global registration is used in various fields such as image fusion[27, 28], remote sensing[29, 30], object recognition[31], and robotic navigation[32]. The problem of establishing the global position in robotic navigation is most similar to global registration in the domain of image-based computer-guided bronchoscopy. In robotic navigation, global registration is also referred to as the “robot kidnapping problem,” wherein the position of a robot has to be estimated when it is moved to any arbitrary pose and no motion estimates are available[32]. Moreno et al. presented a non-linear filter, termed evolutive localization filter, that uses raw sensor data and recursively estimates the current pose[32]. Other methods utilizing multi-hypothesis Kalman filters[33, 34], grid-based probabilistic filters[35] and Monte-Carlo localization[36] methods have also been used for addressing the problem of global registration in robot navigation.
In the domain of medical imaging, global registration has been primarily used for multi-modal registration. Zhang et al. have described an adaptive region-intensity-based ultrasound and computed tomography registration[37]. Munim et al. used vector distance functions for registering magnetic resonance (MR) images of multiple patients[38]. Moghari et al. have described a global registration method for aligning multiple bone fractures to a statistical anatomical atlas model[39]. Principal component analysis and the unscented Kalman filter were used for local and global registration, respectively. Fookes et al. have also described a method for registration of multiple MR images from the same patient by formulating the problem as the minimization of a covariance weighted non-linear least square function[40].
In image-based bronchoscopy guidance, researchers have focused on the problem of local registration. However, few have worked on the problem of global registration, whereby the branch location of the bronchoscope is established. Bricault et al. have proposed a multi-level strategy for registration[23]. In this work, the relative position change of the sub-division wall from one bifurcation to the next was used to identify the branch position of the bronchoscope. Shinohara et al. described a branch identification method using eigenspace-image matching[41]. However, this method addresses bronchoscope tracking and cannot be used for global registration. Moreover, it requires manual initialization.